Progressively Stacking 2.0: A Multi-stage Layerwise Training Method for BERT Training Speedup

• URL: http://arxiv.org/abs/2011.13635v1
• Date: Fri, 27 Nov 2020 10:00:22 GMT
• Title: Progressively Stacking 2.0: A Multi-stage Layerwise Training Method for BERT Training Speedup
• Authors: Cheng Yang, Shengnan Wang, Chao Yang, Yuechuan Li, Ru He, Jingqiao Zhang
• Abstract summary: We propose an efficient multi-stage layerwise training (MSLT) approach to reduce the training time of BERT. In the proposed training strategy, only top few layers participate in backward computation, while most layers only participate in forward computation. Experimental results show that the proposed method can achieve more than 110% training speedup without significant performance degradation.
• Score: 13.50984315473865
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Pre-trained language models, such as BERT, have achieved significant accuracy gain in many natural language processing tasks. Despite its effectiveness, the huge number of parameters makes training a BERT model computationally very challenging. In this paper, we propose an efficient multi-stage layerwise training (MSLT) approach to reduce the training time of BERT. We decompose the whole training process into several stages. The training is started from a small model with only a few encoder layers and we gradually increase the depth of the model by adding new encoder layers. At each stage, we only train the top (near the output layer) few encoder layers which are newly added. The parameters of the other layers which have been trained in the previous stages will not be updated in the current stage. In BERT training, the backward computation is much more time-consuming than the forward computation, especially in the distributed training setting in which the backward computation time further includes the communication time for gradient synchronization. In the proposed training strategy, only top few layers participate in backward computation, while most layers only participate in forward computation. Hence both the computation and communication efficiencies are greatly improved. Experimental results show that the proposed method can achieve more than 110% training speedup without significant performance degradation.

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